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Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem

SWARM AND EVOLUTIONARY COMPUTATION 2015年 23卷 11-26页

作者： Ariyasingha, I. D. I. D. Fernando, T. G. I. Open Univ Sri Lanka Fac Nat Sci Dept Math & Comp Sci Sri Jayawardenepura Sri Lanka Univ Sri Jayewardenepura Fac Sci Appl Dept Comp Sci Sri Jayewardenepura Sri Lanka

Most real world combinatorial optimization problems are difficult to solve with multiple objectives which have to be optimized simultaneously. Over the last few years, researches have been proposed several ant colony optimization algorithms to solve multiple objectives. The aim of this paper is to review the recently proposed multi-objective ant colony optimization (MOACO) algorithms and compare their performances on two, three and four objectives with different numbers of ants and numbers of iterations. Moreover, a detailed analysis is performed for these MOACO algorithms by applying them on several multi-objective benchmark instances of the traveling salesman problem. The results of the analysis have shown that most of the considered MOACO algorithms obtained better performances for more than two objectives and their performance depends slightly on the number of objectives, number of iterations and number of ants used. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Ant colony optimization multi-objective problem Non-dominated solution Pareto optimal front Performance indicator Traveling salesman problem

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multi-objective protein encoding: Redefinition of the problem, new problem-aware operators, and approach based on Variable Neighborhood Search

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INFORMATION SCIENCES 2019年 500卷 173-189页

作者： Gonzalez-Sanchez, Belen Vega-Rodriguez, Miguel A. Santander-Jimenez, Sergio Univ Extremadura Escuela Politecn Avda Univ S-N Caceres 10003 Spain Univ Extremadura Inst Invest Tecnol Informat Aplicadas Extremadura Avda Univ S-N Caceres 10003 Spain Univ Lisbon Inst Super Tecn INESC ID P-1000029 Lisbon Portugal

Synthetic biology is currently involved in improving the expression levels of proteins. A preeminent strategy herein is to encode multiple copies of the same amino acid sequence (protein) and integrate these copies into the genome of an organism. However, this is a challenging task. First, the different protein-coding sequences (CDSs) should be as different as possible to avoid homologous recombination. The second challenge lies in the fact that some synonymous codons are better adapted than others. Therefore, the best adapted synonymous codons should be employed if possible. These two challenges are conflicting and multi-objective optimization has to be consequently used. For this purpose, we design and implement a multi-objective Variable Neighborhood Search (MOVNS) algorithm with new problem-aware mutation operators. Following the related work, we initially define the multi-objective problem based on three optimization objectives. However, after the first experiments, we realize that one of the objectives can be discarded, simplifying the problem without degrading the solutions' quality. The obtained results confirm the attainment of statistically significant improvements in comparison to related works. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Variable Neighborhood Search multi-objective problem Protein encoding Design of multiple genes Protein-coding sequence (CDS)

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A pareto optimal study for the multi-objective oil platform location problem with NSGA-II

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2018年 169卷 258-268页

作者： Oliveira Camara, Marcus Vinicius Ribeiro, Glaydston Mattos Ribeiro Tosta, Marielce de Cassia Fed Univ Rio de Janeiro UFRJ Rio De Janeiro RJ Brazil Fed Univ Espirito Santo UFES Vitoria ES Brazil

Offshore production has a high level of complexity inherent to its development and exploration. This context leads to the need for the multi-objective and multi-level Capacitated Oil Platform Location problem (MMCOPLP), which is used for the definition of places for platforms and drilling of wells, given a set of possible candidate points. The conflict between objectives is commonly present in the multi-objective problems and, consequently, there is a set of optimal solutions called the Pareto Optimal. The Pareto Optimal is hard to be determined by exact methods, and heuristics and/or metaheuristics are implemented as alternative methods. Considering the above, the main objective of this study is to propose a Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve the MMCOPLP. Instances are also proposed and the e-Constraint exact method is used to find the Pareto Optimal. Based on the error ratio, on generational distance and on hypervolume metrics, computational experiments show that the NSGA-II provides good solutions, close to Pareto Optimal and better than a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic proposed in the literature.

关键词： Platform location Oil field development multi-objective problem Petroleum engineering

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Modified γ-operator technique for intuitionistic fuzzy multi-objective nonlinear programming problems with application in agriculture

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Kaur, Shubhpreet Mahajan, Sumati Chauhan, Abhishek Deemed Univ Punjab Engn Coll Dept Math Chandigarh 160012 India

In recent years, intuitionistic fuzzy theory has gained significant attention for its ability to handle uncertainty through both membership and non-membership degrees. This paper presents a novel modification to gamma-operator technique by introducing distinct gamma-operators for membership and non-membership functions to tackle intuitionistic fuzzy environment. The proposed technique is rigorously validated through the proof of relevant theorems that demonstrate its capability to derive efficient solutions for multi-objective nonlinear programming problems, where all parameters are represented as triangular intuitionistic fuzzy numbers. To elucidate the proposed technique, an illustrative example is presented. Furthermore, a comparative study with existing techniques is conducted, which highlights the superior performance of the proposed method. Finally, an application in the agriculture sector demonstrates the practical relevance and effectiveness of the proposed method in real-world scenarios.

关键词： multi-objective problem gamma-operator Nonlinear programming Triangular intuitionistic fuzzy number Agricultural planning

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multi-objective simulation optimization for selection and determination of order quantity in supplier selection problem under uncertainty and quality criteria

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第1-4期93卷 161-173页

作者： Shadkam, Elham Bijari, Mehdi Isfahan Univ Technol Dept Ind & Syst Engn Esfahan Iran

Simulation optimization is providing solutions to practical stochastic problems. Supplier selection is one of the most important decisions that determine the survival of an organization. In this paper, a novel multi-objective simulation optimization method to make decisions on selecting the suppliers and determining the order quantities is proposed. Regarding the fact that a real supply chain is multi-objective with uncertain parameters and includes both quantitative and qualitative variables, the proposed method considers these points and is applicable to real-world problems. This method also considers supplier selection and order quantity allocation to each supplier, which are totally related, as an integrated model. The proposed method consists of four basic modules: Cuckoo Optimization Algorithm (COA), Discrete Event Simulation (DES), Supply Chain Model (SCM), and Generalized Data Envelopment Analysis (GDEA). Unlike many multi-objective methods, the proposed method is not limited to the number of objective functions and this is one of its main benefits. It also pays attention to the efficiency of the organization and, at the same time, finding inputs which result in best output amounts. This method, in addition to the convergence criterion, pays special attention to the dispersion of the Pareto frontier as the second criterion for choosing the good solutions. For implementation of the proposed method, the numerical results for the problem of supplier selection in multi-product, multi-customer modes, and uncertain and qualitative variables are discussed and the Pareto frontiers are presented. The proposed method in this paper is compared with a similar method, and the results show the efficiency of the proposed method.

关键词： Supplier selection problem Simulation optimization multi-objective problem Cuckoo Optimization Algorithm Generalized Data Envelopment Analysis Efficiency

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Integrated approach for dynamic economic/emission dispatch problem: multi-objective moth flame optimizer with transmission loss prediction using cascaded forward neural network

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ELECTRICAL ENGINEERING 2024年第3期106卷 3495-3528页

作者： Nalini, Nagulsamy Kumar, Chandrasekaran Vijayarajan, Periyasamy Chidambararaj, Natarajan M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur Tamil Nadu India Karpagam Coll Engn Dept Elect & Elect Engn Coimbatore Tamil Nadu India Univ Coll Engn Dept Elect & Elect Engn BIT Campus Tiruchirappalli Tamil Nadu India St Josephs Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India

This paper presents improved single- and multi-objective algorithms based on the original moth flame optimizer (MFO) to tackle the dynamic economic emission dispatch (DEED) problem that affects power systems operations. The DEED problem is a multi-objective optimization problem that is strongly constrained, multi-dimensional, nonlinear, and non-convex. It comprises several optimization criteria, many of which are in direct opposition to one another;therefore, no one solution is optimal with regard to all of those criteria. Firstly, an enhanced flame generation strategy is incorporated into the MFO algorithm to improve performance. Then, the improved MFO is combined with the crowding distance mechanism and non-dominated sorting framework to enhance the convergence rate and the quality of the results. This helps improve the convergence pace. Firstly, the proposed multi-objective moth flame optimizer (MOMFO) algorithm is validated using 15 ZDT and UF benchmark multi-objective test functions. Then, the nonlinear DEED problem is also solved by determining the feasible optimal solution using the MOMFO algorithm. The implementation of the MOMFO on 10-unit systems and the IEEE 30-bus test system is being done to display the ability to solve a nonlinear, non-convex, and constrained DEED optimization problem. The DEED problem is solved using the MOMFO algorithm and other state-of-the-art algorithms, such as the non-dominated sorting genetic algorithm-II (NSGA-II), the multi-objective teaching-learning-based optimization (MOTLBO) algorithm, and multi-objective reptile search algorithm (MORSA). The selection of the control parameters of the MOMFO can be decided from the algorithm's findings on different IEEE bus systems. This study also introduces a new technique for incorporating loss predictions using artificial neural networks into the DEED model. During each phase of the dispatch time, the trained neural network can make only a single forecast of the transmission loss. The

关键词： Crowding distance Economic load dispatch Emission dispatch Moth flame optimizer multi-objective problem Non-dominated sorting

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Evaluation of multi-objective inverse heat conduction problem based on particle swarm optimization algorithm, normal distribution and finite element method

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年第PartA期127卷 1114-1127页

作者： Wang, Xiaowei Li, Huiping He, Lianfang Li, Zhichao Shandong Univ Sci & Technol Sch Mat Sci & Engn 579 Qianwangang Rd Qingdao 266590 Shandong Peoples R China

The boundary heat transfer coefficient (BHTC) and thermal conductivity are the most essential thermal physical parameters, which have a significant effect on the calculation accuracy of physical fields in the numerical simulation. To simultaneously estimate the temperature-dependent BHTC and thermal conductivity by solving the inverse heat conduction problem (IHCP), a hybrid method (ZPSO) based on particle swarm optimization algorithm (PSO), normal distribution method and finite element method (FEM) is presented, where normal distribution method representing the transient solution space is used to improve the convergence speed. A two-dimensional direct heat conduction problem (DHCP) with the temperature-dependent thermal-physical parameters is simulated based on FEM. Some temperature curves at certain position of FEM model are attained in the simulation. The temperature-dependent BHTC and thermal conductivity are simultaneously evaluated by PSO-FEM and ZPSO-FEM according to the temperature curves attained in DHCP. Comparing the temperature-dependent thermal-physical parameters attained in the IHCP to that used in the DHCP, the results show that the BHTC and thermal conductivity evaluated by the ZPSO are well consistent with those used in the DHCP. The evaluation results show that the convergence of the hybrid method is well, and the convergence speed is accelerated by the ZPSO. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Inverse heat conduction multi-objective problem Particle swarm optimization algorithm Finite element method

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A two-phase multi-objective metaheuristic for a green UAV grid routing problem

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OPTIMIZATION LETTERS 2023年第9期17卷 2233-2256页

作者： Marques Jr, Elias L. Coelho, Vitor N. Coelho, Igor M. Ochi, Luiz Satoru Maculan, Nelson Mladenovic, Nenad Coelho, Bruno N. Univ Fed Fluminense Inst Comp Sci Niteroi Brazil OptBlocks Av Joao Pinheiro274 Room 201 BR-30130186 Belo Horizonte MG Brazil Univ Fed Rio De Janeiro Rio De Janeiro Brazil Khalifa Univ Abu Dhabi U Arab Emirates Univ Fed Sao Joao Del Rei REDEMAT Sao Joao Del Rei Brazil

This paper deals with Unmanned Aerial Vehicle (UAV) routing in dynamic grid scenarios with limited battery autonomy and multiple charging stations. Inspired by a multi-criteria view of real systems, we consider different objective functions, while respecting the navigation over forbidden areas and also a real-time flight autonomy. A multi-objective variant of Variable Neighborhood Search is considered for finding sets of non-dominated solutions. Twelve neighborhood structures were developed in order to explore the solution space, including learning techniques. The latter stores known routes in order to speed up the search. A case of study was developed where UAVs have to serve clients spread throughout a grid, representing a map. Each UAV starts in a given grid point with a given battery charge, where the grid is composed by four different kinds of points: a regular one and three special (prohibited, recharge and client). Any update can happen on the routes on real-time, so the metaheuristic should handle real-time information and update the plan and graphics user interface accordingly. Any sequence of valid adjacent points forms a route, but since this yields a huge number of combinations, a preprocessing technique is proposed to pre-compute distances in a given dynamic scenario. Computational results demonstrate the performance of different variants of the proposed algorithms.

关键词： Unmanned aerial vehicle Microgrids multi-objective problem Variable neighborhood search Biased random key genetic algorithm

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An analytical hierarchy process-based approach to solve the multi-objective multiple traveling salesman problem

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INTELLIGENT SERVICE ROBOTICS 2018年第4期11卷 355-369页

作者： Trigui, Sahar Cheikhrouhou, Omar Koubaa, Anis Zarrad, Anis Youssef, Habib Univ Manouba ENSI Manouba Tunisia Cooperat Intelligent Networked Syst COINS Res Grp Riyadh Saudi Arabia Taif Univ At Taif Saudi Arabia Univ Sfax Comp & Embedded Syst Lab Sfax Tunisia Prince Sultan Univ Riyadh Saudi Arabia Polytech Inst Porto CISTER INESC TEC ISEP Porto Portugal Univ Birmingham Sch Comp Sci Birmingham W Midlands England Univ Sousse PRINCE Res Lab Sousse Tunisia

We consider the problem of assigning a team of autonomous robots to target locations in the context of a disaster management scenario while optimizing several objectives. This problem can be cast as a multiple traveling salesman problem, where several robots must visit designated locations. This paper provides an analytical hierarchy process (AHP)-based approach to this problem, while minimizing three objectives: the total traveled distance, the maximum tour, and the deviation rate. The AHP-based approach involves three phases. In the first phase, we use the AHP process to define a specific weight for each objective. The second phase consists in allocating the available targets, wherein we define and use three approaches: market-based, robot and task mean allocation-based, and balanced-based. Finally, the third phase involves the improvement in the solutions generated in the second phase. To validate the efficiency of the AHP-based approach, we used MATLAB to conduct an extensive comparative simulation study with other algorithms reported in the literature. The performance comparison of the three approaches shows a gap between the market-based approach and the other two approaches of up to 30%. Further, the results show that the AHP-based approach provides a better balance between the objectives, as compared to other state-of-the-art approaches. In particular, we observed an improvement in the total traveled distance when using the AHP-based approach in comparison with the distance traveled when using a clustering-based approach.

关键词： Assignment MTSP multiple depots multi-objective problem AHP

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multi-objective Transit Route Network Design as Set Covering problem

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2016年第3期17卷 670-679页

作者： Owais, Mahmoud Osman, Mostafa K. Moussa, Ghada Assiut Univ Dept Civil Engn Assiut 71515 Egypt Assiut Univ Fac Comp & Informat Assiut 71515 Egypt

Many past researchers have ignored the multi-objective nature of the transit route network design problem (TrNDP), recognizing user or operator cost as their sole objective. The main purpose of this study is to identify the inherent conflict among TrNDP objectives in the design process. The conventional scheme for transit route design is addressed. A route constructive genetic algorithm is proposed to produce a vast pool of candidate routes that reflect the objectives of design, and then, a set covering problem (SCP) is formulated for the selection stage. A heuristic algorithm based on a randomized priority search is implemented for the SCP to produce a set of nondominated solutions that achieve different tradeoffs among the identified objectives. The solution methodology has been tested using Mandl's benchmark network problem. The test results showed that the methodology developed in this research not only outperforms solutions previously identified in the literature in terms of strategic and tactical terms of design, but it is also able to produce Pareto (or near Pareto) optimal solutions. A real-scale network of Rivera was also tested to prove the proposed methodology's reliability for larger-scale transit networks. Although many efficient meta-heuristics have been presented so far for the TrNDP, the presented one may take the lead because it does not require any weight coefficient calibration to address the multi-objective nature of the problem.

关键词： Meta-heuristics transit route network design multi-objective problem set covering problem

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